This write up aims at understanding the role of big data certification courses chiefly at four stages. The first stage is the preparation stage, the second stage is the training stage, the third stage is the advanced or professional stage and the fourth stage is the development stage. We aim to provide a brief description of all these stages.
The role of certified courses in the career of a data scientist can be understood at four stages:
1- Preparatory stage
The preparation stage is all about choosing the best course and the best institution for data training. This is because there is no dearth of certified courses which are available in the market. So, it is always challenging to choose the best course. It has been observed that students end up choosing a course which is not a great fit to their skills. The panacea is to choose from a course which is perfectly aligned to one’s interests as well as one’s skills.
Let us explain the preparatory stage through an example. A person who has done his bachelor’s degree in computer sciences should choose from the courses in which he already possesses some basic knowledge. This means that he can choose from courses like data structures, cybersecurity, database management and software development. On the other hand, if a person has done his bachelor’s in business management, he can choose from a similar genre of courses. This may include subjects like business intelligence, statistics and aptitude with special applications in artificial intelligence.
2- Training stage
The training stage is aimed to provide new skills to a data scientist. It may also aim to upgrade one’s existing skills.
In the training stage, a person is exposed to a variety of skills and certified courses which includes artificial intelligence, statistics, aptitude training, big data analytics, business management, business intelligence and the like.
Let us now explain this stage through an illustration. The training stage usually involves frequent visits to software parks and working with skilled professionals. It is in this stage that trainees are mentored by business analysts and professionals. The training institutions usually have a collaboration with companies in major fields and trainees come to know about the various projects, products, processes as well as work culture of such companies. In addition to this, the trainees are exposed to advanced tools like python, R programming, development of pseudo codes, hadoop etc.
3- Advanced stage
In the advanced stage, which is primarily an application stage, big data certification begins to give its benefits. These benefits include a job of choice and a sector of comfort. The advanced stage enables a professional to work with advanced tools of software development and hardware testing. In this stage, a person gains exposure to the creative environments of different companies.
The training stage should not be confused with the advanced stage. The difference between the two lies in the nature of work executed by the trainee in the company. In the training stage, the nature of work is generally observatory in nature. In the advanced stage, a trainee is a part and parcel of the company and is usually paid by the company.
4- Development stage
In the development stage, a professional is able to develop his own software projects and work in a research ecosystem. This research environment can provide him access to tools which are used for development of new products. In one word, this stage is the most prospective stage for a data scientist because it is in this stage that the benefits of training are reaped. One successful product of a data scientist can earn him a huge sum of money. In addition to acting as a money spinner, such a product infuses the data scientist with the spirit of new research.
Future prospects
The certification in big data provides a perfect roadmap for development of young data scientists. In the future, big data courses are slated to grow in both size and diversity. So, we need to get involved in such courses as soon as possible.
Concluding remarks
There is a dire need to incorporate some of the most popular data courses in the curriculum so that we become aware about their importance.
